When executives and portfolio managers talk about reading market signals, they typically mean stock prices, bond yields, or survey data. Prediction markets are now entering that conversation – and the volume figures behind them suggest this is not a fringe development.
As of March 2026, the two dominant prediction market platforms, Kalshi and Polymarket, are processing a combined weekly notional volume above $5.3 billion. That number has attracted attention from traders, analysts, and businesses looking for a faster,
crowd-sourced read on what outcomes are likely.
What Prediction Markets Actually Are
A prediction market is a platform where participants buy and sell contracts tied to the outcome of a specific event. The price of a contract reflects the crowd’s estimated probability of that outcome occurring.
If a contract pays $1 if the Federal Reserve holds rates steady, and it trades at $0.96, the market is pricing a 96% probability of no change. That figure updates in real time as new information enters the market.
The mechanism works because participants have a financial stake in being correct. Unlike a poll or an analyst forecast, prediction market pricing reflects money on the line.
The Two Platforms Shaping the Space
The prediction market space is currently defined by two very different platforms, each attracting a distinct participant base.
Kalshi operates as a CFTC-regulated exchange within the US financial system. As of mid-March 2026, it holds over 404,000 active markets and recently posted a record $2.9 billion in weekly notional volume – driven largely by NCAA March Madness activity. Its regulated status makes it the preferred option for US-based participants who require a compliant environment.
Polymarket is a decentralized platform with a global reach. Its rolling 30-day volume of
$9.6 billion reflects 26.9% growth over the prior period. Open interest across both platforms sits at $893 million, with Polymarket holding a slight edge at $463 million.
The platforms often price similar events identically. When they diverge, that gap becomes a research signal in its own right.
For a real-time view across both platforms, including category breakdowns, transaction counts, and contract-level pricing, DeFi Rate’s live dashboard aggregates Kalshi and Polymarket data in one place, updated continuously throughout the trading day.
Where Businesses Are Finding Value
The most immediate use case is as a cross-check on consensus. Before a central bank meeting, a regulatory decision, or an election, a prediction market can reveal whether the public probability matches internal forecasts.
The Fed decision market is the clearest current example. With the March 2026 FOMC meeting concluding today, the Polymarket contract on any rate change has drawn over
$142 million in rolling volume – with 100% of contracts pricing no change. That is not a narrow majority view. The market has settled.
For a business planning around rate-sensitive decisions, that kind of high-conviction, high-liquidity pricing provides a reference point that is harder to dismiss than a single analyst note.
Other common business applications include:
- Political risk assessment – Markets on election outcomes, leadership changes, and policy decisions allow businesses to quantify geopolitical risk rather than simply flagging it as a concern.
- Macro forecasting – Contracts on oil prices, Bitcoin levels, and Fed decisions provide a live probability layer over standard economic models.
- Competitive intelligence – Markets on company-specific events, earnings outcomes, or regulatory decisions can surface crowd sentiment before official announcements.
- Scenario planning – Open interest data reveals where the most capital is concentrated, helping teams understand which scenarios the market is actively hedging.
Key Metrics to Understand
Not all prediction market data carries equal weight. These are the figures that matter most when using the platforms as a research tool.
| Metric | What It Tells You |
| Volume | How much money has traded on a contract – higher volume means more reliable pricing |
| Open Interest | Capital currently at risk on unsettled contracts – indicates how committed participants are |
| Leading Outcome % | The crowd’s current probability estimate |
| Platform | Whether the price comes from a regulated (Kalshi) or decentralized (Polymarket) market |
A contract with $10,000 in volume carries far less analytical weight than one with $140 million. Volume-weighted pricing is the number to watch.
Limitations Worth Factoring In
Prediction markets perform best when a large number of informed participants are trading on a liquid market. In thin or obscure markets, prices can be distorted by a single large position before the crowd corrects it.
They also cannot anticipate events that have not yet entered public consciousness. A true surprise – an unexpected resignation, a sudden geopolitical development – will not show up in market pricing before it happens.
And market mechanics occasionally misfire. When sentiment shifts faster than new information arrives, contracts can overshoot probability estimates before correcting. Treating prediction market data as one input among several, rather than a standalone oracle, is the right approach.
The CFTC’s framework for event contracts sets out the regulatory context for how these markets operate in the US, which is useful background for any compliance or legal team evaluating whether and how to use this data.
The Business Case for Prediction Market Data
Prediction markets do not replace traditional research. They add a layer of real-money crowd intelligence that is updated continuously and reflects a wide range of participants.
At over $5 billion in combined weekly volume and nearly $900 million in open interest, the data these platforms generate is no longer marginal. Businesses that track earnings sentiment, political risk, or macro conditions now have a tool that prices those risks in real time.
The question is not whether prediction markets are worth watching. The question is how to build them into an existing research process in a way that adds signal rather than noise.
For teams that want to start with the data before building a framework around it, monitoring the most liquid contracts – those with seven-figure volume and significant open interest – is the right entry point.
